"""simple docstring"""



import unittest

from diffusers.models.unet_ad_blocks import *  # noqa F403
from diffusers.utils import torch_device

from .test_unet_blocks_common import UNetBlockTesterMixin






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Tuple							  =						DownBlockaD  # noqa F405
 _snake_case       :					Optional[int]							  =						"""down"""


 def 						a__   ( self						):
        __a       =    [-0.0232, -0.9869, 0.8054, -0.0637, -0.1688, -1.4264, 0.4470, -1.3394, 0.0904]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Union[str, Any]							  =						ResnetDownsampleBlockaD  # noqa F405
 _snake_case       :					Union[str, Any]							  =						"""down"""


 def 						a__   ( self						):
        __a       =    [0.0710, 0.2410, -0.7320, -1.0757, -1.1343, 0.3540, -0.0133, -0.2576, 0.0948]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Tuple							  =						AttnDownBlockaD  # noqa F405
 _snake_case       :					Union[str, Any]							  =						"""down"""


 def 						a__   ( self						):
        __a       =    [0.0636, 0.8964, -0.6234, -1.0131, 0.0844, 0.4935, 0.3437, 0.0911, -0.2957]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[Any]							  =						CrossAttnDownBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""down"""


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.2238, -0.7396, -0.2255, -0.3829, 0.1925, 1.1665, 0.0603, -0.7295, 0.1983]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[Any]							  =						SimpleCrossAttnDownBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""down"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_encoder_hidden_states=lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 @unittest.skipIf(torch_device == "mps"    ,		"MPS result is not consistent"						)
 def 						a__   ( self						):
        __a       =    [0.7921, -0.0992, -0.1962, -0.7695, -0.4242, 0.7804, 0.4737, 0.2765, 0.3338]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[int]							  =						SkipDownBlockaD  # noqa F405
 _snake_case       :					int							  =						"""down"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_skip_sample=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [-0.0845, -0.2087, -0.2465, 0.0971, 0.1900, -0.0484, 0.2664, 0.4179, 0.5069]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Any							  =						AttnSkipDownBlockaD  # noqa F405
 _snake_case       :					Dict							  =						"""down"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_skip_sample=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [0.5539, 0.1609, 0.4924, 0.0537, -0.1995, 0.4050, 0.0979, -0.2721, -0.0642]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[int]							  =						DownEncoderBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""down"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_temb=lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    {
            "in_channels": 32,
            "out_channels": 32,
        }
        __a       =    self.dummy_input
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [1.1102, 0.5302, 0.4872, -0.0023, -0.8042, 0.0483, -0.3489, -0.5632, 0.7626]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					int							  =						AttnDownEncoderBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""down"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_temb=lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    {
            "in_channels": 32,
            "out_channels": 32,
        }
        __a       =    self.dummy_input
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.8966, -0.1486, 0.8568, 0.8141, -0.9046, -0.1342, -0.0972, -0.7417, 0.1538]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[str]							  =						UNetMidBlockaD  # noqa F405
 _snake_case       :					Optional[int]							  =						"""mid"""


 def 						a__   ( self						):
        __a       =    {
            "in_channels": 32,
            "temb_channels": 128,
        }
        __a       =    self.dummy_input
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [-0.1062, 1.7248, 0.3494, 1.4569, -0.0910, -1.2421, -0.9984, 0.6736, 1.0028]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[Any]							  =						UNetMidBlockaDCrossAttn  # noqa F405
 _snake_case       :					int							  =						"""mid"""


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.0187, 2.4220, 0.4484, 1.1203, -0.6121, -1.5122, -0.8270, 0.7851, 1.8335]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Tuple							  =						UNetMidBlockaDSimpleCrossAttn  # noqa F405
 _snake_case       :					Optional[int]							  =						"""mid"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_encoder_hidden_states=lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.7143, 1.9974, 0.5448, 1.3977, 0.1282, -1.1237, -1.4238, 0.5530, 0.8880]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Dict							  =						UpBlockaD  # noqa F405
 _snake_case       :					str							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [-0.2041, -0.4165, -0.3022, 0.0041, -0.6628, -0.7053, 0.1928, -0.0325, 0.0523]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Tuple							  =						ResnetUpsampleBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [0.2287, 0.3549, -0.1346, 0.4797, -0.1715, -0.9649, 0.7305, -0.5864, -0.6244]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[Any]							  =						CrossAttnUpBlockaD  # noqa F405
 _snake_case       :					Optional[int]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [-0.1403, -0.3515, -0.0420, -0.1425, 0.3167, 0.5094, -0.2181, 0.5931, 0.5582]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[Any]							  =						SimpleCrossAttnUpBlockaD  # noqa F405
 _snake_case       :					List[Any]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase    ,		include_encoder_hidden_states=lowerCamelCase						)


 def 						a__   ( self						):
        __a ,   __a       =    super().prepare_init_args_and_inputs_for_common()
        __a       =    32
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.2645, 0.1480, 0.0909, 0.8044, -0.9758, -0.9083, 0.0994, -1.1453, -0.7402]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Optional[int]							  =						AttnUpBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)






 @unittest.skipIf(torch_device == "mps"    ,		"MPS result is not consistent"						)
 def 						a__   ( self						):
        __a       =    [0.0979, 0.1326, 0.0021, 0.0659, 0.2249, 0.0059, 0.1132, 0.5952, 0.1033]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Tuple							  =						SkipUpBlockaD  # noqa F405
 _snake_case       :					List[Any]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [-0.0893, -0.1234, -0.1506, -0.0332, 0.0123, -0.0211, 0.0566, 0.0143, 0.0362]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					Union[str, Any]							  =						AttnSkipUpBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_res_hidden_states_tuple=lowerCamelCase						)






 def 						a__   ( self						):
        __a       =    [0.0361, 0.0617, 0.2787, -0.0350, 0.0342, 0.3421, -0.0843, 0.0913, 0.3015]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[str]							  =						UpDecoderBlockaD  # noqa F405
 _snake_case       :					str							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_temb=lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    {"in_channels": 32, "out_channels": 32}

        __a       =    self.dummy_input
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.4404, 0.1998, -0.9886, -0.3320, -0.3128, -0.7034, -0.6955, -0.2338, -0.3137]
        super().test_output(lowerCamelCase						)






class  snake_case__      (       snake_case_,						unittest.TestCase       ):
 _snake_case       :					List[Any]							  =						AttnUpDecoderBlockaD  # noqa F405
 _snake_case       :					List[str]							  =						"""up"""


 @property
 def 						a__   ( self						):
        return super().get_dummy_input(include_temb=lowerCamelCase						)


 def 						a__   ( self						):
        __a       =    {"in_channels": 32, "out_channels": 32}

        __a       =    self.dummy_input
        return init_dict, inputs_dict






 def 						a__   ( self						):
        __a       =    [0.6738, 0.4491, 0.1055, 1.0710, 0.7316, 0.3339, 0.3352, 0.1023, 0.3568]
        super().test_output(lowerCamelCase						)

"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__:Tuple									=			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Union[str, Any]							  =						"""sew-d"""


 def __init__( self    ,		lowerCamelCase=32    ,		lowerCamelCase=768    ,		lowerCamelCase=12    ,		lowerCamelCase=12    ,		lowerCamelCase=3072    ,		lowerCamelCase=2    ,		lowerCamelCase=512    ,		lowerCamelCase=256    ,		lowerCamelCase=True    ,		lowerCamelCase=True    ,		lowerCamelCase=("p2c", "c2p")    ,		lowerCamelCase="layer_norm"    ,		lowerCamelCase="gelu_python"    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.0    ,		lowerCamelCase=0.1    ,		lowerCamelCase=0.02    ,		lowerCamelCase=1E-7    ,		lowerCamelCase=1E-5    ,		lowerCamelCase="group"    ,		lowerCamelCase="gelu"    ,		lowerCamelCase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,		lowerCamelCase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,		lowerCamelCase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,		lowerCamelCase=False    ,		lowerCamelCase=128    ,		lowerCamelCase=16    ,		lowerCamelCase=True    ,		lowerCamelCase=0.05    ,		lowerCamelCase=10    ,		lowerCamelCase=2    ,		lowerCamelCase=0.0    ,		lowerCamelCase=10    ,		lowerCamelCase=0    ,		lowerCamelCase="mean"    ,		lowerCamelCase=False    ,		lowerCamelCase=False    ,		lowerCamelCase=256    ,		lowerCamelCase=0    ,		lowerCamelCase=1    ,		lowerCamelCase=2    ,		**lowerCamelCase    ,		):
        super().__init__(**lowerCamelCase    ,		pad_token_id=lowerCamelCase    ,		bos_token_id=lowerCamelCase    ,		eos_token_id=lowerCamelCase						)
        __a       =    hidden_size
        __a       =    feat_extract_norm
        __a       =    feat_extract_activation
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    list(lowerCamelCase						)
        __a       =    conv_bias
        __a       =    num_conv_pos_embeddings
        __a       =    num_conv_pos_embedding_groups
        __a       =    len(self.conv_dim						)
        __a       =    num_hidden_layers
        __a       =    intermediate_size
        __a       =    squeeze_factor
        __a       =    max_position_embeddings
        __a       =    position_buckets
        __a       =    share_att_key
        __a       =    relative_attention
        __a       =    norm_rel_ebd
        __a       =    list(lowerCamelCase						)
        __a       =    hidden_act
        __a       =    num_attention_heads
        __a       =    hidden_dropout
        __a       =    attention_dropout
        __a       =    activation_dropout
        __a       =    feat_proj_dropout
        __a       =    final_dropout
        __a       =    layer_norm_eps
        __a       =    feature_layer_norm_eps
        __a       =    initializer_range
        __a       =    vocab_size

        if (
            (len(self.conv_stride						) != self.num_feat_extract_layers)
            or (len(self.conv_kernel						) != self.num_feat_extract_layers)
            or (len(self.conv_dim						) != self.num_feat_extract_layers)
        ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F"but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)"
                   F"= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`."						)

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __a       =    apply_spec_augment
        __a       =    mask_time_prob
        __a       =    mask_time_length
        __a       =    mask_time_min_masks
        __a       =    mask_feature_prob
        __a       =    mask_feature_length
        __a       =    mask_feature_min_masks

        # ctc loss
        __a       =    ctc_loss_reduction
        __a       =    ctc_zero_infinity

        # sequence classification
        __a       =    use_weighted_layer_sum
        __a       =    classifier_proj_size






 @property
 def 						a__   ( self						):
        return functools.reduce(operator.mul    ,		self.conv_stride    ,		1						)
